Journal
2023 IEEE INTERNATIONAL WORKSHOP ON METROLOGY FOR LIVING ENVIRONMENT, METROLIVENV
Volume -, Issue -, Pages 102-106Publisher
IEEE
DOI: 10.1109/MetroLivEnv56897.2023.10164043
Keywords
monitoring system; building diagnostic; IoT system; sensor network; self-sensing materials
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The paper introduces a monitoring system for the built environment using electrical impedance sensors and develops an early warning system for decision-making in a seismic context. Preliminary data on mortar specimens were collected to predict the electrical impedance module using a Neural Prophet-based deep learning model. The results can be used to support decision-making strategies for building management with acceptable accuracy.
The aim of this paper is to present a monitoring system for the built environment based on electrical impedance sensors, together with the development of an early warning system to support decision-making processes in a seismic context. In particular, preliminary data were collected on mortar specimens embedding stainless-steel electrodes for the periodic measurement of electrical impedance. Hence, these data were exploited to train a Neural Prophet-based deep learning model for the prediction of the electrical impedance module. Indeed, this quantity can provide a lot of information about the health status of the monitored structures. The results can be exploited for the development of an early warning system supporting decision-making strategies for the building management. The model can predict the trend of electrical impedance with acceptable accuracy (MAPE <2%); hence, the monitoring platform can provide information suitable for the development of an early warning system.
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